ArticlePLoS computational biology2022
Experimental guidance for discovering genetic networks through hypothesis reduction on time series.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- The Emergent Architecture of Autonomous Clocks Described by Computational Algorithms of Regulatory Networks.Advances in experimental medicine and biology · 2026Article
- Evolution of the Cell-Cycle Clock Models: Biochemical or Transcriptional?Advances in experimental medicine and biology · 2026Review
- Genetic network structure and dynamics: identifying simple negative feedback loops.Interface focus · 2025Article
- Inferring causal trajectories from spatial transcriptomics using CASCAT.Nucleic acids research · 2025Article
- Identification and correction of time-series transcriptomic anomalies.Nucleic acids research · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.